An efficient phishing website detection plugin service was developed using machine learning technique based on the prevalent phishing threat while using existing web browsers in critical online transactions. The study gathered useful information from 27 published articles and dataset consisting of 11,000 data points with 30 features downloaded from phishtank. A unique architectural framework for detecting phishing websites was designed using random forest machine learning classifier based the aim and objectives of the study. The model was trained with 90% (9,900) of the dataset and tested with 10% (1,100) using Python programming language for better efficiency. Microsoft Visual Studio Code, Jupiter Notebook, Anaconda Integrated Development Environment, HTML/CSS and JavaScript was used in developing the frontend of the model for easy integration into existing web browsers. The proposed model was also modeled using use-case and sequence diagrams to test its internal functionalities. The result revealed that the proposed model had an accuracy of 0.96, error rate of 0.04, precision of 0.97, recall value of 0.99 and f1-score of 0.98 which far outperform other models developed based on literatures. Future recommendations should focus on improved security features, more phishing adaptive learning properties, and so on, so that it can be reasonably applied to other web browsers in accurately detecting real-world phishing situations using advanced algorithms such as hybridized machine learning and deep learning techniques.

Currently, the planet population is terrified of the deaths of more than 4 million people from the coronavirus as they do not know that, according to the WHO, about 8 million of the population die annually in silence from urban atmosphere pollution by and with hazardous substances and particulate matters from the industry and automobile transport operation. These materials show the results of Russian studies proving that current urban pollution shall be defined not only by hazardous substances and particulate matters emitted with vehicle exhaust gases, but also by particulate matters from vehicle operation, first of all, from asphalt roadway wear, from tyre wear and from brake systems wear, which are not legally regulated either by nations or at the international level (UN Regulations) yet. The Russian studies (2015-2017) are presented regarding the comparative analysis of average emissions of particulate matters less than 2.5 microns (µm) from different sources: with exhaust gases (EG) (25%); from wear of brake systems (5%); from wear of tyres (8%) and from wear of roadways (65%), which were substantially confirmed by the studies conducted in Great Britain: from EG-32%; from tyres-18%; from brakes-18% and from wear of roadways-40%. Based on these results of the comprehensive studies, calculations of economic damage caused by the ecological situations and technogenic disasters of the current and future periods analyzed above, which amount to 65 quadrillion (65•10 15) US dollars for the today's world and ca. 100 million dollars for the Russian Federation. According to the data of the World Health Organization (WHO), as of 2018, 9 out of 10 people around the world breathe air with high concentrations of pollutants. For that very reason, from 7 to 8 million people die annually because of the consequences of breathing the air containing particulate matters less than 2.5-10 µm in size which are able to penetrate deep inside the lungs and cardiovascular system, causing such diseases as stroke, cardiac diseases, lung cancer, chronic obstructive pulmonary disease and respiratory infections, including pneumonia.

Musicology is a growing focus in computer science. Past research has had success in automatically generating music through learning-based agents that make use of neural networks and through model and rule-based approaches. These methods require a significant amount of information, either in the form of a large dataset for learning or a comprehensive set of rules based on musical concepts. This paper explores a model in which a minimal amount of musical information is needed to compose a desired style of music. This paper takes from two concepts, objectness, and evolutionary computation. The concept of objectness, an idea directly derived from imagery and pattern recognition, was used to extract specific musical objects from single musical inputs which are then used as the foundation to algorithmically produce musical pieces that are similar in style to the original inputs. These musical pieces are the product of evolutionary algorithms which implement a sequential evolution approach wherein a generated output may or may not yet be fully within the fitness thresholds of the input pieces. This method eliminates the need for a large amount of pre-provided data as well as the need for long processing times that are commonly associated with machine-learned art-pieces. This study aims to show a proof of concept of the implementation of the described model.

Artificial intelligence (AI) is a prominent force in the twenty-first century. Organizations have more data than ever before, therefore it's critical that the analytics team distinguishes between Interesting Data and Useful Data. "Feature Selection" and "Feature Extraction" are two critical parts of Machine Learning. We are now witnessing the emergence of the fourth industrial revolution, as well as a significant number of evolutionary changes in machine learning methodologies to achieve operational excellence in the operation and maintenance of industrial assets in an efficient, reliable, safe, and cost-effective manner. Knowledge-based systems, expert systems, artificial neural networks, genetic algorithms, fuzzy logic, case-based reasoning, and any combination of these approaches are examples of AI methodologies (hybrid systems), machine learning, and biomimicry, such as swarm intelligence and distributed intelligence, are being employed by multidisciplinary researchers to tackle a wide range of previously intractable challenges related with proactive maintenance management of industrial assets. The purpose of this study is to examine the function of artificial intelligence in the condition monitoring and diagnostic engineering management of modern engineering assets. The study also emphasizes the dangers of unethical and immoral usage of AI.

Machine learning (ML) is the study of computer algorithms that can improve automatically through experience and by the use of data artificial intelligence is associated with it. Machine learning algorithms create a model based on training data to make predictions or judgments without having to be explicitly programmed to do so. Machine learning algorithms are utilized in a wide range of applications, including medicine, email filtering, speech recognition, and computer vision, where developing traditional algorithms to do the required tasks is difficult or impossible.